{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "tqdm.pandas()\n",
    "from keras.preprocessing import image\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'Attractive' #set this to Attractive or Babyface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns = 1000\n",
    "pd.options.display.max_rows = 1000\n",
    "pd.options.display.max_seq_items = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://chicagofaces.org/default/\n",
    "cfd_df_raw = pd.read_csv(\"CFD_Version_203/metadata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target</th>\n",
       "      <th>Race</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>NumberofRaters</th>\n",
       "      <th>Female_prop</th>\n",
       "      <th>Male_prop</th>\n",
       "      <th>Asian_prop</th>\n",
       "      <th>Black_prop</th>\n",
       "      <th>Latino_prop</th>\n",
       "      <th>Multi_prop</th>\n",
       "      <th>Other_prop</th>\n",
       "      <th>White_prop</th>\n",
       "      <th>Afraid</th>\n",
       "      <th>Angry</th>\n",
       "      <th>Attractive</th>\n",
       "      <th>Babyface</th>\n",
       "      <th>Disgusted</th>\n",
       "      <th>Dominant</th>\n",
       "      <th>Feminine</th>\n",
       "      <th>Happy</th>\n",
       "      <th>Masculine</th>\n",
       "      <th>Prototypic</th>\n",
       "      <th>Sad</th>\n",
       "      <th>Suitability</th>\n",
       "      <th>Surprised</th>\n",
       "      <th>Threatening</th>\n",
       "      <th>Trustworthy</th>\n",
       "      <th>Unusual</th>\n",
       "      <th>Luminance_median</th>\n",
       "      <th>Nose_Width</th>\n",
       "      <th>Nose_Length</th>\n",
       "      <th>Lip_Thickness</th>\n",
       "      <th>Face_Length</th>\n",
       "      <th>R_Eye_H</th>\n",
       "      <th>L_Eye_H</th>\n",
       "      <th>Avg_Eye_Height</th>\n",
       "      <th>R_Eye_W</th>\n",
       "      <th>L_Eye_W</th>\n",
       "      <th>Avg_Eye_Width</th>\n",
       "      <th>Face_Width_Cheeks</th>\n",
       "      <th>Face_Width_Mouth</th>\n",
       "      <th>Forehead</th>\n",
       "      <th>Pupil_Top_R</th>\n",
       "      <th>Pupil_Top_L</th>\n",
       "      <th>Asymmetry_pupil_top</th>\n",
       "      <th>Pupil_Lip_R</th>\n",
       "      <th>Pupil_Lip_L</th>\n",
       "      <th>Asymmetry_pupil_lip</th>\n",
       "      <th>BottomLip_Chin</th>\n",
       "      <th>Midcheek_Chin_R</th>\n",
       "      <th>Midcheek_Chin_L</th>\n",
       "      <th>Cheeks_avg</th>\n",
       "      <th>Midbrow_Hairline_R</th>\n",
       "      <th>Midbrow_Hairline_L</th>\n",
       "      <th>Faceshape</th>\n",
       "      <th>Heartshapeness</th>\n",
       "      <th>Noseshape</th>\n",
       "      <th>LipFullness</th>\n",
       "      <th>EyeShape</th>\n",
       "      <th>EyeSize</th>\n",
       "      <th>UpperHeadLength</th>\n",
       "      <th>MidfaceLength</th>\n",
       "      <th>ChinLength</th>\n",
       "      <th>ForeheadHeight</th>\n",
       "      <th>CheekboneHeight</th>\n",
       "      <th>CheekboneProminence</th>\n",
       "      <th>FaceRoundness</th>\n",
       "      <th>fWHR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AF-200</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>32.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.035714</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>4.111111</td>\n",
       "      <td>2.857143</td>\n",
       "      <td>1.392857</td>\n",
       "      <td>1.928571</td>\n",
       "      <td>5.629630</td>\n",
       "      <td>2.928571</td>\n",
       "      <td>1.357143</td>\n",
       "      <td>3.659574</td>\n",
       "      <td>1.892857</td>\n",
       "      <td>.</td>\n",
       "      <td>2.821429</td>\n",
       "      <td>1.321429</td>\n",
       "      <td>3.925926</td>\n",
       "      <td>2.518519</td>\n",
       "      <td>174.0</td>\n",
       "      <td>230.5</td>\n",
       "      <td>250.5</td>\n",
       "      <td>135.5</td>\n",
       "      <td>1071.0</td>\n",
       "      <td>63.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>65.25</td>\n",
       "      <td>163.0</td>\n",
       "      <td>159.5</td>\n",
       "      <td>161.25</td>\n",
       "      <td>676.0</td>\n",
       "      <td>584.5</td>\n",
       "      <td>443.5</td>\n",
       "      <td>424.5</td>\n",
       "      <td>428.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>350.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>406.5</td>\n",
       "      <td>415.75</td>\n",
       "      <td>277.5</td>\n",
       "      <td>289.5</td>\n",
       "      <td>0.631186</td>\n",
       "      <td>1.156544</td>\n",
       "      <td>0.920160</td>\n",
       "      <td>0.126517</td>\n",
       "      <td>0.404651</td>\n",
       "      <td>0.060924</td>\n",
       "      <td>0.414099</td>\n",
       "      <td>0.326797</td>\n",
       "      <td>0.130719</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.388189</td>\n",
       "      <td>91.5</td>\n",
       "      <td>0.545752</td>\n",
       "      <td>1.921146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AF-201</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>27</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.814815</td>\n",
       "      <td>3.111111</td>\n",
       "      <td>3.111111</td>\n",
       "      <td>2.481481</td>\n",
       "      <td>2.592593</td>\n",
       "      <td>2.111111</td>\n",
       "      <td>4.111111</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>3.893617</td>\n",
       "      <td>3.960000</td>\n",
       "      <td>.</td>\n",
       "      <td>1.370370</td>\n",
       "      <td>1.851852</td>\n",
       "      <td>3.538462</td>\n",
       "      <td>2.038462</td>\n",
       "      <td>172.0</td>\n",
       "      <td>233.5</td>\n",
       "      <td>235.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>46.50</td>\n",
       "      <td>144.0</td>\n",
       "      <td>138.5</td>\n",
       "      <td>141.25</td>\n",
       "      <td>688.0</td>\n",
       "      <td>542.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>432.5</td>\n",
       "      <td>453.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>366.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>160.5</td>\n",
       "      <td>427.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>426.00</td>\n",
       "      <td>319.5</td>\n",
       "      <td>348.5</td>\n",
       "      <td>0.619820</td>\n",
       "      <td>1.269373</td>\n",
       "      <td>0.993617</td>\n",
       "      <td>0.116216</td>\n",
       "      <td>0.329204</td>\n",
       "      <td>0.041892</td>\n",
       "      <td>0.414414</td>\n",
       "      <td>0.329279</td>\n",
       "      <td>0.144595</td>\n",
       "      <td>0.300901</td>\n",
       "      <td>0.383784</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0.488288</td>\n",
       "      <td>1.901129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AF-202</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>24.448276</td>\n",
       "      <td>29</td>\n",
       "      <td>0.827586</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>0.310345</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.448276</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.464286</td>\n",
       "      <td>1.862069</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.344828</td>\n",
       "      <td>1.551724</td>\n",
       "      <td>2.862069</td>\n",
       "      <td>3.206897</td>\n",
       "      <td>2.379310</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.808511</td>\n",
       "      <td>1.931034</td>\n",
       "      <td>.</td>\n",
       "      <td>1.551724</td>\n",
       "      <td>2.034483</td>\n",
       "      <td>3.379310</td>\n",
       "      <td>2.413793</td>\n",
       "      <td>153.5</td>\n",
       "      <td>277.5</td>\n",
       "      <td>268.0</td>\n",
       "      <td>141.5</td>\n",
       "      <td>1245.5</td>\n",
       "      <td>66.5</td>\n",
       "      <td>62.0</td>\n",
       "      <td>64.25</td>\n",
       "      <td>177.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>179.50</td>\n",
       "      <td>657.5</td>\n",
       "      <td>599.5</td>\n",
       "      <td>512.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>385.5</td>\n",
       "      <td>387.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>494.50</td>\n",
       "      <td>367.5</td>\n",
       "      <td>376.0</td>\n",
       "      <td>0.527900</td>\n",
       "      <td>1.096747</td>\n",
       "      <td>1.035448</td>\n",
       "      <td>0.113609</td>\n",
       "      <td>0.357939</td>\n",
       "      <td>0.051586</td>\n",
       "      <td>0.411080</td>\n",
       "      <td>0.310317</td>\n",
       "      <td>0.173424</td>\n",
       "      <td>0.298475</td>\n",
       "      <td>0.397029</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.481333</td>\n",
       "      <td>1.888249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AF-203</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>22.758621</td>\n",
       "      <td>29</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.758621</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.586207</td>\n",
       "      <td>1.655172</td>\n",
       "      <td>3.275862</td>\n",
       "      <td>3.862069</td>\n",
       "      <td>1.620690</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>1.724138</td>\n",
       "      <td>2.531915</td>\n",
       "      <td>2.344828</td>\n",
       "      <td>.</td>\n",
       "      <td>1.931034</td>\n",
       "      <td>1.178571</td>\n",
       "      <td>3.793103</td>\n",
       "      <td>2.241379</td>\n",
       "      <td>175.5</td>\n",
       "      <td>233.5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>129.5</td>\n",
       "      <td>1083.5</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.5</td>\n",
       "      <td>69.25</td>\n",
       "      <td>155.0</td>\n",
       "      <td>157.5</td>\n",
       "      <td>156.25</td>\n",
       "      <td>629.5</td>\n",
       "      <td>542.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>462.5</td>\n",
       "      <td>450.0</td>\n",
       "      <td>456.25</td>\n",
       "      <td>298.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>0.580988</td>\n",
       "      <td>1.161439</td>\n",
       "      <td>0.972917</td>\n",
       "      <td>0.119520</td>\n",
       "      <td>0.443200</td>\n",
       "      <td>0.063913</td>\n",
       "      <td>0.354407</td>\n",
       "      <td>0.343793</td>\n",
       "      <td>0.169820</td>\n",
       "      <td>0.272266</td>\n",
       "      <td>0.421089</td>\n",
       "      <td>87.5</td>\n",
       "      <td>0.500231</td>\n",
       "      <td>1.863719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AF-204</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>30.137931</td>\n",
       "      <td>29</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.827586</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>0.103448</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.551724</td>\n",
       "      <td>1.586207</td>\n",
       "      <td>3.172414</td>\n",
       "      <td>2.068966</td>\n",
       "      <td>1.655172</td>\n",
       "      <td>1.758621</td>\n",
       "      <td>4.629630</td>\n",
       "      <td>1.275862</td>\n",
       "      <td>1.482759</td>\n",
       "      <td>2.574468</td>\n",
       "      <td>3.517241</td>\n",
       "      <td>.</td>\n",
       "      <td>1.137931</td>\n",
       "      <td>1.206897</td>\n",
       "      <td>3.310345</td>\n",
       "      <td>1.517241</td>\n",
       "      <td>168.5</td>\n",
       "      <td>252.5</td>\n",
       "      <td>257.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1179.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>63.00</td>\n",
       "      <td>150.0</td>\n",
       "      <td>141.5</td>\n",
       "      <td>145.75</td>\n",
       "      <td>679.0</td>\n",
       "      <td>605.5</td>\n",
       "      <td>517.5</td>\n",
       "      <td>475.5</td>\n",
       "      <td>480.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>335.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>212.5</td>\n",
       "      <td>433.0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>438.50</td>\n",
       "      <td>339.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>0.575912</td>\n",
       "      <td>1.121387</td>\n",
       "      <td>0.980583</td>\n",
       "      <td>0.084818</td>\n",
       "      <td>0.432247</td>\n",
       "      <td>0.053435</td>\n",
       "      <td>0.438931</td>\n",
       "      <td>0.293045</td>\n",
       "      <td>0.180237</td>\n",
       "      <td>0.293893</td>\n",
       "      <td>0.371925</td>\n",
       "      <td>73.5</td>\n",
       "      <td>0.513571</td>\n",
       "      <td>1.935783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Target Race Gender        Age  NumberofRaters  Female_prop  Male_prop  \\\n",
       "0  AF-200    A      F  32.571429              28     1.000000   0.000000   \n",
       "1  AF-201    A      F  23.666667              27     1.000000   0.000000   \n",
       "2  AF-202    A      F  24.448276              29     0.827586   0.172414   \n",
       "3  AF-203    A      F  22.758621              29     1.000000   0.000000   \n",
       "4  AF-204    A      F  30.137931              29     1.000000   0.000000   \n",
       "\n",
       "   Asian_prop  Black_prop  Latino_prop  Multi_prop  Other_prop  White_prop  \\\n",
       "0    1.000000    0.000000     0.000000    0.000000    0.000000         0.0   \n",
       "1    0.962963    0.000000     0.000000    0.037037    0.000000         0.0   \n",
       "2    0.310345    0.068966     0.137931    0.448276    0.034483         0.0   \n",
       "3    0.758621    0.000000     0.068966    0.172414    0.000000         0.0   \n",
       "4    0.827586    0.000000     0.068966    0.103448    0.000000         0.0   \n",
       "\n",
       "     Afraid     Angry  Attractive  Babyface  Disgusted  Dominant  Feminine  \\\n",
       "0  2.035714  1.571429    4.111111  2.857143   1.392857  1.928571  5.629630   \n",
       "1  1.814815  3.111111    3.111111  2.481481   2.592593  2.111111  4.111111   \n",
       "2  1.464286  1.862069    3.000000  2.344828   1.551724  2.862069  3.206897   \n",
       "3  2.586207  1.655172    3.275862  3.862069   1.620690  1.750000  5.000000   \n",
       "4  2.551724  1.586207    3.172414  2.068966   1.655172  1.758621  4.629630   \n",
       "\n",
       "      Happy  Masculine  Prototypic       Sad Suitability  Surprised  \\\n",
       "0  2.928571   1.357143    3.659574  1.892857           .   2.821429   \n",
       "1  1.555556   1.666667    3.893617  3.960000           .   1.370370   \n",
       "2  2.379310   3.000000    1.808511  1.931034           .   1.551724   \n",
       "3  2.571429   1.724138    2.531915  2.344828           .   1.931034   \n",
       "4  1.275862   1.482759    2.574468  3.517241           .   1.137931   \n",
       "\n",
       "   Threatening  Trustworthy   Unusual  Luminance_median  Nose_Width  \\\n",
       "0     1.321429     3.925926  2.518519             174.0       230.5   \n",
       "1     1.851852     3.538462  2.038462             172.0       233.5   \n",
       "2     2.034483     3.379310  2.413793             153.5       277.5   \n",
       "3     1.178571     3.793103  2.241379             175.5       233.5   \n",
       "4     1.206897     3.310345  1.517241             168.5       252.5   \n",
       "\n",
       "   Nose_Length  Lip_Thickness  Face_Length  R_Eye_H  L_Eye_H  Avg_Eye_Height  \\\n",
       "0        250.5          135.5       1071.0     63.5     67.0           65.25   \n",
       "1        235.0          129.0       1110.0     44.0     49.0           46.50   \n",
       "2        268.0          141.5       1245.5     66.5     62.0           64.25   \n",
       "3        240.0          129.5       1083.5     68.0     70.5           69.25   \n",
       "4        257.5          100.0       1179.0     66.0     60.0           63.00   \n",
       "\n",
       "   R_Eye_W  L_Eye_W  Avg_Eye_Width  Face_Width_Cheeks  Face_Width_Mouth  \\\n",
       "0    163.0    159.5         161.25              676.0             584.5   \n",
       "1    144.0    138.5         141.25              688.0             542.0   \n",
       "2    177.0    182.0         179.50              657.5             599.5   \n",
       "3    155.0    157.5         156.25              629.5             542.0   \n",
       "4    150.0    141.5         145.75              679.0             605.5   \n",
       "\n",
       "   Forehead  Pupil_Top_R  Pupil_Top_L  Asymmetry_pupil_top  Pupil_Lip_R  \\\n",
       "0     443.5        424.5        428.0                  3.5        350.0   \n",
       "1     460.0        432.5        453.0                 20.5        366.0   \n",
       "2     512.0        492.0        490.0                  2.0        385.5   \n",
       "3     384.0        402.0        393.0                  9.0        370.0   \n",
       "4     517.5        475.5        480.0                  4.5        335.0   \n",
       "\n",
       "   Pupil_Lip_L  Asymmetry_pupil_lip  BottomLip_Chin  Midcheek_Chin_R  \\\n",
       "0        350.0                  0.0           140.0            425.0   \n",
       "1        365.0                  1.0           160.5            427.0   \n",
       "2        387.5                  2.0           216.0            500.0   \n",
       "3        375.0                  5.0           184.0            462.5   \n",
       "4        356.0                 21.0           212.5            433.0   \n",
       "\n",
       "   Midcheek_Chin_L  Cheeks_avg  Midbrow_Hairline_R  Midbrow_Hairline_L  \\\n",
       "0            406.5      415.75               277.5               289.5   \n",
       "1            425.0      426.00               319.5               348.5   \n",
       "2            489.0      494.50               367.5               376.0   \n",
       "3            450.0      456.25               298.0               292.0   \n",
       "4            444.0      438.50               339.0               354.0   \n",
       "\n",
       "   Faceshape  Heartshapeness  Noseshape  LipFullness  EyeShape   EyeSize  \\\n",
       "0   0.631186        1.156544   0.920160     0.126517  0.404651  0.060924   \n",
       "1   0.619820        1.269373   0.993617     0.116216  0.329204  0.041892   \n",
       "2   0.527900        1.096747   1.035448     0.113609  0.357939  0.051586   \n",
       "3   0.580988        1.161439   0.972917     0.119520  0.443200  0.063913   \n",
       "4   0.575912        1.121387   0.980583     0.084818  0.432247  0.053435   \n",
       "\n",
       "   UpperHeadLength  MidfaceLength  ChinLength  ForeheadHeight  \\\n",
       "0         0.414099       0.326797    0.130719        0.264706   \n",
       "1         0.414414       0.329279    0.144595        0.300901   \n",
       "2         0.411080       0.310317    0.173424        0.298475   \n",
       "3         0.354407       0.343793    0.169820        0.272266   \n",
       "4         0.438931       0.293045    0.180237        0.293893   \n",
       "\n",
       "   CheekboneHeight  CheekboneProminence  FaceRoundness      fWHR  \n",
       "0         0.388189                 91.5       0.545752  1.921146  \n",
       "1         0.383784                146.0       0.488288  1.901129  \n",
       "2         0.397029                 58.0       0.481333  1.888249  \n",
       "3         0.421089                 87.5       0.500231  1.863719  \n",
       "4         0.371925                 73.5       0.513571  1.935783  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfd_df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(597, 69)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfd_df_raw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getFileNames(target):\n",
    "    files = []\n",
    "    file_count = 0\n",
    "    path = \"CFD_Version_203/CFD_203_Images/%s/\" % (target)\n",
    "    for r, d, f in os.walk(path):\n",
    "        for file in f: #BF-001 has several images\n",
    "            if ('.jpg' in file) or ('.jpeg' in file) or '.png' in file:\n",
    "                files.append(file)\n",
    "    return files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfd_df_raw[\"files\"] = cfd_df_raw.Target.apply(getFileNames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Target</th>\n",
       "      <th>files</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AF-200</td>\n",
       "      <td>[CFD-AF-200-228-N.jpg]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AF-201</td>\n",
       "      <td>[CFD-AF-201-060-N.jpg]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AF-202</td>\n",
       "      <td>[CFD-AF-202-122-N.jpg]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AF-203</td>\n",
       "      <td>[CFD-AF-203-077-N.jpg]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AF-204</td>\n",
       "      <td>[CFD-AF-204-067-N.jpg]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Target                   files\n",
       "0  AF-200  [CFD-AF-200-228-N.jpg]\n",
       "1  AF-201  [CFD-AF-201-060-N.jpg]\n",
       "2  AF-202  [CFD-AF-202-122-N.jpg]\n",
       "3  AF-203  [CFD-AF-203-077-N.jpg]\n",
       "4  AF-204  [CFD-AF-204-067-N.jpg]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfd_df_raw[['Target', 'files']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfd_instances = []\n",
    "for index, instance in cfd_df_raw.iterrows():\n",
    "    folder = instance.Target\n",
    "    score = instance[target]\n",
    "    for file in instance.files:\n",
    "        tmp_instance = []\n",
    "        #tmp_instance.append((target, file, score))\n",
    "        tmp_instance.append(folder)\n",
    "        tmp_instance.append(file)\n",
    "        tmp_instance.append(score)\n",
    "        cfd_instances.append(tmp_instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(cfd_instances, columns = [\"folder\", \"file\", \"score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CFD-AF-200-228-N.jpg</td>\n",
       "      <td>4.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CFD-AF-201-060-N.jpg</td>\n",
       "      <td>3.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CFD-AF-202-122-N.jpg</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CFD-AF-203-077-N.jpg</td>\n",
       "      <td>3.275862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CFD-AF-204-067-N.jpg</td>\n",
       "      <td>3.172414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   file     score\n",
       "0  CFD-AF-200-228-N.jpg  4.111111\n",
       "1  CFD-AF-201-060-N.jpg  3.111111\n",
       "2  CFD-AF-202-122-N.jpg  3.000000\n",
       "3  CFD-AF-203-077-N.jpg  3.275862\n",
       "4  CFD-AF-204-067-N.jpg  3.172414"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['file', 'score']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1207, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findEmotion(file):\n",
    "    #file = CFD-WM-040-023-HO.jpg\n",
    "    file_name = file.split(\".\")[0] #[1] is jpg\n",
    "    emotion = file_name.split(\"-\")[4]\n",
    "    return emotion\n",
    "\n",
    "def findRace(file):\n",
    "    #file = CFD-WM-040-023-HO.jpg\n",
    "    file_name = file.split(\".\")[0] #[1] is jpg\n",
    "    race = file_name.split(\"-\")[1][0]\n",
    "    return race\n",
    "\n",
    "def findGender(file):\n",
    "    #file = CFD-WM-040-023-HO.jpg\n",
    "    file_name = file.split(\".\")[0] #[1] is jpg\n",
    "    gender = file_name.split(\"-\")[1][1]\n",
    "    return gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['emotion'] = df.file.apply(findEmotion)\n",
    "df['race'] = df.file.apply(findRace)\n",
    "df['gender'] = df.file.apply(findGender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#include neutral, happen open mouth and happy close mouth\n",
    "df = df[(df.emotion == 'N') | (df.emotion == 'HO') | (df.emotion == 'HC')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df[df.folder == 'WM-040']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(904, 6)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['file'] = \"CFD_Version_203/CFD_203_Images/\"+df[\"folder\"]+\"/\"+df['file']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>folder</th>\n",
       "      <th>file</th>\n",
       "      <th>score</th>\n",
       "      <th>emotion</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AF-200</td>\n",
       "      <td>CFD_Version_203/CFD_203_Images/AF-200/CFD-AF-2...</td>\n",
       "      <td>4.111111</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AF-201</td>\n",
       "      <td>CFD_Version_203/CFD_203_Images/AF-201/CFD-AF-2...</td>\n",
       "      <td>3.111111</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AF-202</td>\n",
       "      <td>CFD_Version_203/CFD_203_Images/AF-202/CFD-AF-2...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AF-203</td>\n",
       "      <td>CFD_Version_203/CFD_203_Images/AF-203/CFD-AF-2...</td>\n",
       "      <td>3.275862</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AF-204</td>\n",
       "      <td>CFD_Version_203/CFD_203_Images/AF-204/CFD-AF-2...</td>\n",
       "      <td>3.172414</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   folder                                               file     score  \\\n",
       "0  AF-200  CFD_Version_203/CFD_203_Images/AF-200/CFD-AF-2...  4.111111   \n",
       "1  AF-201  CFD_Version_203/CFD_203_Images/AF-201/CFD-AF-2...  3.111111   \n",
       "2  AF-202  CFD_Version_203/CFD_203_Images/AF-202/CFD-AF-2...  3.000000   \n",
       "3  AF-203  CFD_Version_203/CFD_203_Images/AF-203/CFD-AF-2...  3.275862   \n",
       "4  AF-204  CFD_Version_203/CFD_203_Images/AF-204/CFD-AF-2...  3.172414   \n",
       "\n",
       "  emotion race gender  \n",
       "0       N    A      F  \n",
       "1       N    A      F  \n",
       "2       N    A      F  \n",
       "3       N    A      F  \n",
       "4       N    A      F  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrievePixels(path):\n",
    "    img = image.load_img(path, grayscale=False, target_size=(224, 224))\n",
    "    x = image.img_to_array(img).reshape(1, -1)[0]\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 904/904 [00:55<00:00, 17.55it/s]\n"
     ]
    }
   ],
   "source": [
    "df['pixels'] = df['file'].progress_apply(retrievePixels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>folder</th>\n",
       "      <th>file</th>\n",
       "      <th>score</th>\n",
       "      <th>emotion</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AF-200</td>\n",
       "      <td>CFD_Version_203/CFD_203_Images/AF-200/CFD-AF-2...</td>\n",
       "      <td>4.111111</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>[255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AF-201</td>\n",
       "      <td>CFD_Version_203/CFD_203_Images/AF-201/CFD-AF-2...</td>\n",
       "      <td>3.111111</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>[255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AF-202</td>\n",
       "      <td>CFD_Version_203/CFD_203_Images/AF-202/CFD-AF-2...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>[255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AF-203</td>\n",
       "      <td>CFD_Version_203/CFD_203_Images/AF-203/CFD-AF-2...</td>\n",
       "      <td>3.275862</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>[255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AF-204</td>\n",
       "      <td>CFD_Version_203/CFD_203_Images/AF-204/CFD-AF-2...</td>\n",
       "      <td>3.172414</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
       "      <td>F</td>\n",
       "      <td>[255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   folder                                               file     score  \\\n",
       "0  AF-200  CFD_Version_203/CFD_203_Images/AF-200/CFD-AF-2...  4.111111   \n",
       "1  AF-201  CFD_Version_203/CFD_203_Images/AF-201/CFD-AF-2...  3.111111   \n",
       "2  AF-202  CFD_Version_203/CFD_203_Images/AF-202/CFD-AF-2...  3.000000   \n",
       "3  AF-203  CFD_Version_203/CFD_203_Images/AF-203/CFD-AF-2...  3.275862   \n",
       "4  AF-204  CFD_Version_203/CFD_203_Images/AF-204/CFD-AF-2...  3.172414   \n",
       "\n",
       "  emotion race gender                                             pixels  \n",
       "0       N    A      F  [255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255...  \n",
       "1       N    A      F  [255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255...  \n",
       "2       N    A      F  [255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255...  \n",
       "3       N    A      F  [255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255...  \n",
       "4       N    A      F  [255.0, 255.0, 255.0, 255.0, 255.0, 255.0, 255...  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CFD_Version_203/CFD_203_Images/WF-022/CFD-WF-022-019-HO.jpg\n",
      "Attractiveness score:  5.0869565219999995\n",
      "-------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CFD_Version_203/CFD_203_Images/WF-003/CFD-WF-003-004-HO.jpg\n",
      "Attractiveness score:  4.89010989\n",
      "-------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CFD_Version_203/CFD_203_Images/WF-024/CFD-WF-024-006-HO.jpg\n",
      "Attractiveness score:  4.755319149\n",
      "-------------------\n"
     ]
    }
   ],
   "source": [
    "for index, instance in df[(df.race == 'W') #A: Asian, B: Black, L: Latino, W: White]\n",
    "                          & (df.gender == 'F') # F: Female / M: Male\n",
    "                          & (df.emotion == 'HO') #HO: Happy Open Mouth, HC: Happy Closed Mouth, N: Neutral\n",
    "                         ].sort_values(by=['score'], ascending = False).head(3).iterrows():\n",
    "#for index, instance in df.sort_values(by=['score'], ascending = False).head(3).iterrows():\n",
    "    img = instance.pixels\n",
    "    img = img.reshape(224, 224, 3)\n",
    "    img = img / 255\n",
    "    \n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "    print(instance.file)\n",
    "    print(\"Attractiveness score: \",instance.score)\n",
    "    \n",
    "    print(\"-------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-process features and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = []\n",
    "pixels = df['pixels'].values\n",
    "for i in range(0, pixels.shape[0]):\n",
    "    features.append(pixels[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = np.array(features)\n",
    "features = features.reshape(features.shape[0], 224, 224, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = features / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(904, 224, 224, 3)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we have a few instances. that's why, do not use test and val sets differently\n",
    "train_x, val_x, train_y, val_y = train_test_split(features, df.score.values, test_size=0.2, random_state=17)\n",
    "#test_x, val_x, test_y, val_y = train_test_split(val_x, val_y, test_size=0.50, random_state=17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train set:  723\n",
      "validation set:  181\n"
     ]
    }
   ],
   "source": [
    "print(\"train set: \", train_x.shape[0])\n",
    "print(\"validation set: \", val_x.shape[0])\n",
    "#print(\"test set: \", test_x.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.preprocessing import image\n",
    "from keras.callbacks import ModelCheckpoint,EarlyStopping\n",
    "from keras.layers import Dense, Activation, Dropout, Flatten, Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Activation\n",
    "from keras.layers import Conv2D, AveragePooling2D\n",
    "from keras.models import Model, Sequential"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Base VGG-Face Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#VGG-Face model\n",
    "base_model = Sequential()\n",
    "base_model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))\n",
    "base_model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
    "base_model.add(ZeroPadding2D((1,1)))\n",
    "base_model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
    "base_model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    " \n",
    "base_model.add(ZeroPadding2D((1,1)))\n",
    "base_model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
    "base_model.add(ZeroPadding2D((1,1)))\n",
    "base_model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
    "base_model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    " \n",
    "base_model.add(ZeroPadding2D((1,1)))\n",
    "base_model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
    "base_model.add(ZeroPadding2D((1,1)))\n",
    "base_model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
    "base_model.add(ZeroPadding2D((1,1)))\n",
    "base_model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
    "base_model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    " \n",
    "base_model.add(ZeroPadding2D((1,1)))\n",
    "base_model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "base_model.add(ZeroPadding2D((1,1)))\n",
    "base_model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "base_model.add(ZeroPadding2D((1,1)))\n",
    "base_model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "base_model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    " \n",
    "base_model.add(ZeroPadding2D((1,1)))\n",
    "base_model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "base_model.add(ZeroPadding2D((1,1)))\n",
    "base_model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "base_model.add(ZeroPadding2D((1,1)))\n",
    "base_model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
    "base_model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
    " \n",
    "base_model.add(Convolution2D(4096, (7, 7), activation='relu'))\n",
    "base_model.add(Dropout(0.5))\n",
    "base_model.add(Convolution2D(4096, (1, 1), activation='relu'))\n",
    "base_model.add(Dropout(0.5))\n",
    "base_model.add(Convolution2D(2622, (1, 1)))\n",
    "base_model.add(Flatten())\n",
    "base_model.add(Activation('softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pre-trained weights of vgg-face model. \n",
    "#you can find it here: https://drive.google.com/file/d/1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo/view?usp=sharing\n",
    "#related blog post: https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/\n",
    "base_model.load_weights('vgg_face_weights.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_of_classes = 1 #this is a regression problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#freeze all layers of VGG-Face except last 7 one\n",
    "for layer in base_model.layers[:-7]:\n",
    "    layer.trainable = False\n",
    "\n",
    "base_model_output = Sequential()\n",
    "base_model_output = Flatten()(base_model.layers[-4].output)\n",
    "base_model_output = Dense(num_of_classes)(base_model_output)\n",
    "\n",
    "attractiveness_model = Model(inputs=base_model.input, outputs=base_model_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sgd = keras.optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9)\n",
    "#attractiveness_model.compile(loss='mean_squared_error', optimizer=sgd)\n",
    "\n",
    "attractiveness_model.compile(loss='mean_squared_error', optimizer=keras.optimizers.Adam())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpointer = ModelCheckpoint(\n",
    "    filepath='%s.hdf5' % (target)\n",
    "    , monitor = \"val_loss\"\n",
    "    , verbose=1\n",
    "    , save_best_only=True\n",
    "    , mode = 'auto'\n",
    ")\n",
    "\n",
    "earlyStop = EarlyStopping(monitor='val_loss', patience=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "score = attractiveness_model.fit(\n",
    "    train_x, train_y\n",
    "    , epochs=5000\n",
    "    , validation_data=(val_x, val_y)\n",
    "    , callbacks=[checkpointer, earlyStop]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_iteration = np.argmin(score.history['val_loss'])+1\n",
    "\n",
    "val_scores = score.history['val_loss'][0:best_iteration]\n",
    "train_scores = score.history['loss'][0:best_iteration]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(val_scores, label='val_loss')\n",
    "plt.plot(train_scores, label='train_loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#restore the best weights\n",
    "from keras.models import load_model\n",
    "attractiveness_model = load_model(\"%s.hdf5\" % (target))\n",
    "attractiveness_model.save_weights('%s.h5' % (target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#actuals = test_y\n",
    "#predictions = attractiveness_model.predict(test_x)\n",
    "predictions = attractiveness_model.predict(val_x)\n",
    "actuals = val_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "perf = pd.DataFrame(actuals, columns = [\"actuals\"])\n",
    "perf[\"predictions\"] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pearson correlation:  0.7876312255072905\n",
      "mae:  0.32754033469477695\n",
      "rmse:  0.44623163710909775\n"
     ]
    }
   ],
   "source": [
    "print(\"pearson correlation: \",perf[['actuals', 'predictions']].corr(method ='pearson').values[0,1])\n",
    "print(\"mae: \", mean_absolute_error(actuals, predictions))\n",
    "print(\"rmse: \", sqrt(mean_squared_error(actuals, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fcbb2e7cb70>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_limit = df.score.min(); max_limit = df.score.max()\n",
    "best_predictions = []\n",
    "\n",
    "#for i in np.arange(1, 7, 0.01):\n",
    "for i in np.arange(int(min_limit), int(max_limit)+1, 0.01):\n",
    "    best_predictions.append(round(i, 2))\n",
    "\n",
    "plt.scatter(best_predictions, best_predictions, s=1, color = 'black', alpha=0.5)\n",
    "plt.scatter(predictions, actuals, s=20, alpha=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMDd Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io\n",
    "mat = scipy.io.loadmat('../imdb_crop/imdb.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"dob\", \"photo_taken\", \"full_path\", \"gender\", \"name\", \"face_location\", \"face_score\", \"second_face_score\", \"celeb_names\", \"celeb_id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "instances = mat['imdb'][0][0][0].shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_df = pd.DataFrame(index = range(0,instances), columns = columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(460723, 10)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in mat:\n",
    "    if i == \"imdb\":\n",
    "        current_array = mat[i][0][0]\n",
    "        for j in range(len(current_array)):\n",
    "            #print(j,\". \",columns[j],\": \",current_array[j][0])\n",
    "            imdb_df[columns[j]] = pd.DataFrame(current_array[j][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imdb_df = imdb_df[imdb_df['gender'] == 0] #just ladies\n",
    "#imdb_df = imdb_df[imdb_df['gender'] == 1] #just men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove pictures does not include face\n",
    "imdb_df = imdb_df[imdb_df['face_score'] != -np.inf]\n",
    "\n",
    "#some pictures include more than one face, remove them\n",
    "imdb_df = imdb_df[imdb_df['second_face_score'].isna()]\n",
    "\n",
    "#check threshold\n",
    "imdb_df = imdb_df[imdb_df['face_score'] >= 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(95234, 10)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extractNames(name):\n",
    "    return name[0]\n",
    "\n",
    "imdb_df['celebrity_name'] = imdb_df['name'].apply(extractNames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "def datenum_to_datetime(datenum):\n",
    "    try:\n",
    "        days = datenum % 1\n",
    "        hours = days % 1 * 24\n",
    "        minutes = hours % 1 * 60\n",
    "        seconds = minutes % 1 * 60\n",
    "        exact_date = datetime.fromordinal(int(datenum)) \\\n",
    "        + timedelta(days=int(days)) + timedelta(hours=int(hours)) \\\n",
    "        + timedelta(minutes=int(minutes)) + timedelta(seconds=round(seconds)) \\\n",
    "        - timedelta(days=366)\n",
    "        year = exact_date.year\n",
    "    except:\n",
    "        year = -1\n",
    "    return year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_df['date_of_birth'] = imdb_df['dob'].apply(datenum_to_datetime)\n",
    "imdb_df = imdb_df[imdb_df.date_of_birth > 0]\n",
    "imdb_df['age'] = imdb_df.photo_taken - imdb_df.date_of_birth      \n",
    "imdb_df = imdb_df[(imdb_df.age >= 18) & (imdb_df.age <= 50)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dob</th>\n",
       "      <th>photo_taken</th>\n",
       "      <th>full_path</th>\n",
       "      <th>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>face_location</th>\n",
       "      <th>face_score</th>\n",
       "      <th>second_face_score</th>\n",
       "      <th>celeb_names</th>\n",
       "      <th>celeb_id</th>\n",
       "      <th>celebrity_name</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>702986</td>\n",
       "      <td>1974</td>\n",
       "      <td>[02/nm0000002_rm221957120_1924-9-16_1974.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[Lauren Bacall]</td>\n",
       "      <td>[[3173.144692593433, 401.0408365741791, 4059.1...</td>\n",
       "      <td>4.096431</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[A.J. Trauth]</td>\n",
       "      <td>11516</td>\n",
       "      <td>Lauren Bacall</td>\n",
       "      <td>1924</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>702986</td>\n",
       "      <td>1974</td>\n",
       "      <td>[02/nm0000002_rm238734336_1924-9-16_1974.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[Lauren Bacall]</td>\n",
       "      <td>[[2135.2177839424635, 765.8537799794512, 3499....</td>\n",
       "      <td>4.865421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[AJ Bowen]</td>\n",
       "      <td>11516</td>\n",
       "      <td>Lauren Bacall</td>\n",
       "      <td>1924</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>706650</td>\n",
       "      <td>1963</td>\n",
       "      <td>[03/nm0000003_rm1883436032_1934-9-28_1963.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[Brigitte Bardot]</td>\n",
       "      <td>[[4.054, 652.694, 1167.552, 1816.192]]</td>\n",
       "      <td>3.056358</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Aaron Seltzer]</td>\n",
       "      <td>2585</td>\n",
       "      <td>Brigitte Bardot</td>\n",
       "      <td>1934</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>706650</td>\n",
       "      <td>1963</td>\n",
       "      <td>[03/nm0000003_rm2034430976_1934-9-28_1963.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[Brigitte Bardot]</td>\n",
       "      <td>[[1045.2505006892818, 350.00950022976065, 1564...</td>\n",
       "      <td>3.776049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Aaron Stanford]</td>\n",
       "      <td>2585</td>\n",
       "      <td>Brigitte Bardot</td>\n",
       "      <td>1934</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>706650</td>\n",
       "      <td>1965</td>\n",
       "      <td>[03/nm0000003_rm3428825856_1934-9-28_1965.jpg]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[Brigitte Bardot]</td>\n",
       "      <td>[[246.62203248711663, 246.62203248711663, 561....</td>\n",
       "      <td>4.260661</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Abdallah El Akal]</td>\n",
       "      <td>2585</td>\n",
       "      <td>Brigitte Bardot</td>\n",
       "      <td>1934</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dob  photo_taken                                       full_path  \\\n",
       "12  702986         1974   [02/nm0000002_rm221957120_1924-9-16_1974.jpg]   \n",
       "14  702986         1974   [02/nm0000002_rm238734336_1924-9-16_1974.jpg]   \n",
       "47  706650         1963  [03/nm0000003_rm1883436032_1934-9-28_1963.jpg]   \n",
       "50  706650         1963  [03/nm0000003_rm2034430976_1934-9-28_1963.jpg]   \n",
       "64  706650         1965  [03/nm0000003_rm3428825856_1934-9-28_1965.jpg]   \n",
       "\n",
       "    gender               name  \\\n",
       "12     0.0    [Lauren Bacall]   \n",
       "14     0.0    [Lauren Bacall]   \n",
       "47     0.0  [Brigitte Bardot]   \n",
       "50     0.0  [Brigitte Bardot]   \n",
       "64     0.0  [Brigitte Bardot]   \n",
       "\n",
       "                                        face_location  face_score  \\\n",
       "12  [[3173.144692593433, 401.0408365741791, 4059.1...    4.096431   \n",
       "14  [[2135.2177839424635, 765.8537799794512, 3499....    4.865421   \n",
       "47             [[4.054, 652.694, 1167.552, 1816.192]]    3.056358   \n",
       "50  [[1045.2505006892818, 350.00950022976065, 1564...    3.776049   \n",
       "64  [[246.62203248711663, 246.62203248711663, 561....    4.260661   \n",
       "\n",
       "    second_face_score         celeb_names  celeb_id   celebrity_name  \\\n",
       "12                NaN       [A.J. Trauth]     11516    Lauren Bacall   \n",
       "14                NaN          [AJ Bowen]     11516    Lauren Bacall   \n",
       "47                NaN     [Aaron Seltzer]      2585  Brigitte Bardot   \n",
       "50                NaN    [Aaron Stanford]      2585  Brigitte Bardot   \n",
       "64                NaN  [Abdallah El Akal]      2585  Brigitte Bardot   \n",
       "\n",
       "    date_of_birth  age  \n",
       "12           1924   50  \n",
       "14           1924   50  \n",
       "47           1934   29  \n",
       "50           1934   29  \n",
       "64           1934   31  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getOriginalSize(image_path):\n",
    "    img = image.load_img(\"../imdb_crop/%s\" % image_path[0], grayscale=False)\n",
    "    x = image.img_to_array(img).reshape(1, -1)[0]\n",
    "    return x.shape[0]\n",
    "    \n",
    "def getImagePixels(image_path):\n",
    "    img = image.load_img(\"../imdb_crop/%s\" % image_path[0], grayscale=False, target_size=(224, 224, 3))\n",
    "    x = image.img_to_array(img).reshape(1, -1)[0]\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 79091/79091 [05:25<00:00, 243.04it/s] \n"
     ]
    }
   ],
   "source": [
    "imdb_df['pixels'] = imdb_df['full_path'].progress_apply(getImagePixels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 79091/79091 [04:45<00:00, 276.74it/s]\n"
     ]
    }
   ],
   "source": [
    "imdb_df['num_of_pixels'] = imdb_df['full_path'].progress_apply(getOriginalSize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_features = []\n",
    "pixels = imdb_df['pixels'].values\n",
    "for i in range(0, pixels.shape[0]):\n",
    "    imdb_features.append(pixels[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_features = np.array(imdb_features)\n",
    "imdb_features = imdb_features.reshape(imdb_features.shape[0], 224, 224, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_features = imdb_features / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_df['attractiveness_score'] = attractiveness_model.predict(imdb_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_df_raw = imdb_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "#restore\n",
    "#imdb_df = imdb_df_raw.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "dropDuplicates = False\n",
    "\n",
    "if dropDuplicates:\n",
    "    print(imdb_df.shape)\n",
    "    imdb_df_best = imdb_df.groupby(\"celebrity_name\", as_index=False)[\"attractiveness_score\"].max()\n",
    "    imdb_df = imdb_df.merge(imdb_df_best, on =[\"celebrity_name\", \"attractiveness_score\"], how=\"inner\")\n",
    "    print(imdb_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_df = imdb_df.sort_values(by=['attractiveness_score'], ascending=False)\n",
    "imdb_df = imdb_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rebecca Hall  ( 17/nm0356017_rm1243584256_1982-5-3_2008.jpg ) -  26  years old\n",
      "Attractiveness score:  83.57\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------\n",
      "Christina Aguilera  ( 94/nm0004694_rm2469435904_1980-12-18_2005.jpg ) -  25  years old\n",
      "Attractiveness score:  78.49\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------\n"
     ]
    }
   ],
   "source": [
    "tmp_df = imdb_df[imdb_df.gender == 0] #0: woman, 1: man\n",
    "tmp_df = tmp_df[tmp_df.num_of_pixels >= 100000]\n",
    "tmp_df = tmp_df[(tmp_df.age >= 21) & (tmp_df.age <= 35)]\n",
    "tmp_df = tmp_df[tmp_df.face_score >= 4]\n",
    "tmp_df = tmp_df.reset_index(drop=True)\n",
    "\n",
    "for index, instance in tmp_df[(tmp_df.index >= 0) & (tmp_df.index < 3)].iterrows():\n",
    "    \n",
    "    print(instance[\"name\"][0],\" (\",instance.full_path[0],\") - \",instance.age,\" years old\")\n",
    "    \n",
    "    attractiveness_score = instance.attractiveness_score\n",
    "    \n",
    "    attractiveness_score = 100 * (attractiveness_score - min_limit) / (max_limit - min_limit)\n",
    "    attractiveness_score = round(attractiveness_score, 2)\n",
    "\n",
    "    print(\"Attractiveness score: \", attractiveness_score)\n",
    "    \n",
    "    img = instance[\"pixels\"]\n",
    "    img = img.reshape(224, 224, 3)\n",
    "    img = img / 255\n",
    "    \n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"-----------------\")\n",
    "    #break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"hello, world!\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
